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X-WR-CALNAME:IORA - Institute of Operations Research and Analytics
X-ORIGINAL-URL:https://iora.nus.edu.sg
X-WR-CALDESC:Events for IORA - Institute of Operations Research and Analytics
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DTSTART:20200101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20210903T100000
DTEND;TZID=Asia/Singapore:20210903T110000
DTSTAMP:20260405T194256
CREATED:20210812T022859Z
LAST-MODIFIED:20210827T060534Z
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SUMMARY:IORA Seminar Series – Ilya O. Ryzhov
DESCRIPTION:Ilya O. Ryzhov is an Associate Professor of Operations Management and Management Science at the Robert H. Smith School of Business\, University of Maryland. His research focuses on decision-making under uncertainty with applications in business analytics. He is an Associate Editor at Operations Research\, and received the 2017 INFORMS Simulation Society Outstanding Publication Award\, as well as the 2020 INFORMS Urban Transportation SIG Outstanding Paper Award. \n  \n\n\n\nName of Speaker\nA/P Ilya O. Ryzhov\n\n\nSchedule\nFriday 3 September 2021\, 10am\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZ0lce-grTMpHNKr4ldsTPL-uZEjwfAgtNAx\n\n\nTitle\nData-driven robust resource allocation with monotonic cost functions\n\n\nAbstract\nThis work illustrates the potential of statistical methods in operations research problems. We consider a two-stage planning problem (arising\, e.g.\, in city logistics) where a resource is first divided among a set of independent regions\, and then costs are incurred based on the allocation to each region. Costs are decreasing in the quantity of the resource\, but their precise values are unknown. We develop a new data-driven uncertainty model for monotonic cost functions\, which can be used in conjunction with robust optimization to obtain tractable allocation decisions that significantly improve worst-case performance outcomes.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-ilya-o-ryzhov/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/png:https://iora.nus.edu.sg/wp-content/uploads/2021/08/Ilya_Rhyzov.png
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20210910T100000
DTEND;TZID=Asia/Singapore:20210910T110000
DTSTAMP:20260405T194256
CREATED:20210812T023152Z
LAST-MODIFIED:20210902T050131Z
UID:14183-1631268000-1631271600@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Dennis Zhang
DESCRIPTION:Dennis Zhang is a tenured associate professor of Operations and Manufacturing Management at the Olin Business School. A/P Zhang’s research focuses on data-driven operations in digital economy and platforms. He implements field experiments and use observational data to improve operations. \n  \n\n\n\nName of Speaker\nA/P Dennis Zhang\n\n\nSchedule\nFriday 10 September\, 10am\n\n\nRegistration Link\nhttps://nus-sg.zoom.us/meeting/register/tZUkdOCgrDkoHtKyWVqFtDPx_-nGbnESs2tL\n\n\nTitle\nChoice Overload with Search Cost and Anticipated Regret: Theoretical Framework and Field Evidence\n\n\nAbstract\nAs consumers are offered an ever-increasing number of options for almost every purchase decision in online retail\, understanding the impact of assortment size on consumer choice decisions––especially on both search and purchase behavior––is critical. Our research speaks to this question by combining empirical analyses with theoretical modeling. First\, via a large-scale field experiment involving $1.6$ million consumers on Alibaba’s online retail platforms\, we causally examine how consumers’ click and purchase behavior changes as the number of products in a choice set increases. We document that consumers’ likelihood of clicking or purchasing at least one product increases at first but then decreases as the number of offered products rises. To explain this inverted-U-shaped relationship\, we develop a “consider-then-choose-with-regret” (CTCR) choice model that incorporates consumers’ search cost and anticipated regret. Numerical experiments suggest that our CTCR model leads to smaller optimal assortments containing products of higher expected utilities and lower prices on average than the classical multinomial logit choice model. Altogether\, this work presents real-world experimental evidence for choice overload on both search and purchase behaviour\, advances the field’s understanding of how assortment sizes alter consumer choices\, and provides a theoretical foundation for incorporating the choice overload effect in operational decisions. \nhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3890056\n\n\n\n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-dennis-zhang/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/08/Dennis-Zhang.jpg
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20210917T100000
DTEND;TZID=Asia/Singapore:20210917T110000
DTSTAMP:20260405T194256
CREATED:20210812T024436Z
LAST-MODIFIED:20210910T064137Z
UID:14186-1631872800-1631876400@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Chen Ningyuan
DESCRIPTION:Dr. Ningyuan Chen is currently an assistant professor at the Department of Management at the University of Toronto Mississauga and cross-appointed at the Rotman School of Management\, University of Toronto. Before joining the University of Toronto\, he was an assistant professor at the Hong Kong University of Science and Technology. He received his Ph.D. from the Industrial Engineering and Operations Research (IEOR) department at Columbia University in 2015. He is interested in various approaches to making data-driven decisions in applications including revenue management. His research is supported by the UGC of Hong Kong and the Discovery Grants Program of Canada. \n  \n\n\n\nName of Speaker\nDr Chen Ningyuan\n\n\nSchedule\nFriday 17 September\, 10am\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZ0td-ysrz8uGtLavCw5BEWFm37wQmWdiVsx\n\n\nTitle\nModel-Free Assortment Pricing with Transaction Data\n\n\nAbstract\nWe study the problem when a firm sets prices for products based on the transaction data\, i.e.\, which product past customers chose from an assortment and what were the historical prices that they observed. Our approach does not impose a model on the distribution of the customers’ valuations and only assumes\, instead\, that purchase choices satisfy incentive-compatible constraints. The individual valuation of each past customer can then be encoded as a polyhedral set\, and our approach maximizes the worst-case revenue assuming that new customers’ valuations are drawn from the empirical distribution implied by the collection of such polyhedra. We show that the optimal prices in this setting can be approximated at any arbitrary precision by solving a compact mixed-integer linear program. Moreover\, we study the single-product case and relate it to the traditional model-based approach. We also design three approximation strategies that are of low computational complexity and interpretable. Comprehensive numerical studies based on synthetic and real data suggest that our pricing approach is uniquely beneficial when the historical data has a limited size or is susceptible to model misspecification.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-chen-ningyuan/
CATEGORIES:IORA Seminar Series
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20210924T100000
DTEND;TZID=Asia/Singapore:20210924T110000
DTSTAMP:20260405T194256
CREATED:20210910T064606Z
LAST-MODIFIED:20210919T133956Z
UID:14399-1632477600-1632481200@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Michael Choi
DESCRIPTION:Michael Choi is currently an Assistant Professor at the Yale-NUS College\, and by courtesy\, at the Department of Statistics and Data Science at National University of Singapore (NUS). He is also affiliated with the Institute of Operations Research and Analytics (iORA). Prior to joining Yale-NUS\, he was an Assistant Professor for three years in the School of Data Science at The Chinese University of Hong Kong\, Shenzhen. He received his PhD in Operations Research from Cornell University in 2017\, and his undergraduate degree in Actuarial Science (First Class Honours) from The University of Hong Kong in 2013. \nHis research interests centre around stochastic processes and their broad applications and intersections with other fields such as data science\, with a particular focus on Markov chains theory and stochastic algorithms driven by Markov chains. He has published extensively in leading journals of his area\, including Transactions of the American Mathematical Society\, Stochastic Processes and their Applications\, Combinatorics\, Probability and Computing\, and Electronic Communications in Probability. \n\n\n\nName of Speaker\nDr Michael Choi\n\n\nSchedule\nFriday 24 September\, 10am\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZ0ofu6hqzkiGtFQQExJuuRXSZnKC2bA9CO_\n\n\nTitle\nOn the convergence of an improved and adaptive kinetic simulated annealing\n\n\nAbstract\nInspired by the work of [Fang et al.. An improved annealing method and its large-time behaviour. Stochastic Process. Appl. (1997)\, Volume 71 Issue 1 Page 55-74.]\, who propose an improved simulated annealing algorithm based on a variant of overdamped Langevin diffusion with state-dependent diffusion coefficient\, we cast this idea in the kinetic setting and develop an improved kinetic simulated annealing (IKSA) method for minimizing a target function U. To analyze its convergence\, we utilize the framework recently introduced by [Monmarché. Hypocoercivity in metastable settings and kinetic simulated annealing. Probab. Theory Related Fields (2018)\, Volume 172 Page 1215-1248.] for the case of kinetic simulated annealing (KSA). The core idea of IKSA rests on introducing a parameter c > inf U\, which de facto modifies the optimization landscape and clips the critical height in IKSA at a maximum of c – inf U. Consequently IKSA enjoys improved convergence with faster logarithmic cooling than KSA. To tune the parameter c\, we propose an adaptive method that we call IAKSA which utilizes the running minimum generated by the algorithm on the fly\, thus avoiding the need to manually adjust c for better performance. We present positive numerical results on some standard global optimization benchmark functions that verify the improved convergence of IAKSA over other Langevin-based annealing methods.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-michael-choi/
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